Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

Purpose Of Review: This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption.

Recent Findings: With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs.

Noninvasive cuffless blood pressure estimation using pulse transit time, Womersley number, and photoplethysmogram intensity ratio.

Objective: Continuous cuffless blood pressure (BP) monitoring research has emerged as blood pressure is one of the dynamic parameters which reflects cardiac arrhythmias and rheological disorders without the drawbacks of current techniques. All the existing measurement techniques are cuff-based with drawbacks such as being discontinuous in nature, being uncomfortable for the patient, etc. Therefore, the goal is to develop an algorithm to estimate BP accurately using the pulse transit time (PTT), photoplethysmogram intensity ratio (PIR) and Womersley number (α) in a noninvasive way.